Summary Our benchmarking reveals a surprising truth: in the race to translate massive perturbation datasets into discovery, the most effective mathematical "lens" isn't the most complex one. While sophisticated metrics like Wasserstein or Mean Pairwise are often favored due to their mathematical impressiveness, we found that E-distance and Euclidean distance provide the superior balance of speed and signal resolution for high-throughput pipelines. By delivering sharper biological contrast at a

In this blog, we examine how the “perturbation effect” can vary depending on the metrics used to define it, and why these differences matter. While these metrics may appear interchangeable, they often capture fundamentally different aspects of the underlying biology. As Perturb-seq datasets continue to grow exponentially, understanding how perturbation effects are measured becomes critical for reliable downstream analysis. When suppression is not an on-off switch In 2025, Nadig and colleagues

The Virtual Cell is a concept at the intersection of computational biology and systems science. At its core, it aims to represent a predictive model of how a living cell responds to internal and external cues. In its ideal form, a Virtual Cell captures every molecular detail - dynamic proteins, metabolic fluxes, physical interactions, and more. Building such a fully mechanistic model remains beyond current computational capabilities. A more tractable approximation has emerged over the past deca